DisplayCast is a many to many Intranet screen sharing system. Its screen capture mechanism creates a sequence of pixmap images of the screen updates. Prior systems that used a similar approach were designed to operate over constrained wide-area networks and did not exploit the Intranet network conditions to achieve high capture rates. First we empirically analyzed the screen contents for a variety of scenarios. We showed that screen updates were sporadic with long periods of inactivity. When active, screens were updated at far higher rates than was supported by earlier systems. The mismatch was pronounced for interactive scenarios. Even during active screen updates, the number of updated pixels were frequently small. We showed that crucial information can be lost if individual updates were merged. When the available system resources could not support high capture rates, we showed ways in which updates can be effectively collapsed. Next, we investigate compression mechanisms for streaming these updates. Even while using a hardware encoder, lossy compressors such as H.264 were unable to sustain high frame rates. Though Zlib lossless compression operated within the latency and compression rate requirements, the compression efficiency was poor. By analyzing the screen pixels, we developed a practical transformation that significantly improved compression rates. DisplayCast incorporates these observations. It shares the processor and network resources required for screen capture, compression and transmission with host applications whose output needs to be shared. DisplayCast is agile and uses faster processing capability to achieve even higher performance. Our system components operate natively in Windows 7, Mac OS X and iOS and is deployed in a production setting. DisplayCast is released under a New BSD License.

Faithful sharing of screen contents is an important collaboration feature. Prior systems were designed to operate over constrained networks. They performed poorly even without such bottlenecks. To build a high performance screen sharing system, we empirically analyzed screen contents for a variety of scenarios. We showed that screen updates were sporadic with long periods of inactivity. When active, screens were updated at far higher rates than was supported by earlier systems. The mismatch was pronounced for interactive scenarios. Even during active screen updates, the number of updated pixels were frequently small. We showed that crucial information can be lost if individual updates were merged. When the available system resources could not support high capture rates, we showed ways in which updates can be effectively collapsed. We showed that Zlib lossless compression performed poorly for screen updates. By analyzing the screen pixels, we developed a practical transformation that significantly improved compression rates. Our system captured 240 updates per second while only using 4.6 Mbps for interactive scenarios. Still, while playing movies in fullscreen mode, our approach could not achieve higher capture rates than prior systems; the CPU remains the bottleneck. A system that incorporates our findings is deployed within the lab.

This paper presents a camera system called FlySPEC. In contrast to a traditional camera system that provides the same video stream to every user, FlySPEC can simultaneously serve different video-viewing requests. This flexibility allows users to conveniently participate in a seminar or meeting at their own pace. Meanwhile, the FlySPEC system provides a seamless blend of manual control and automation. With this control mix, users can easily make tradeoffs between video capture effort and video quality. The FlySPEC camera is constructed by installing a set of pan/tilt/zoom (PTZ) cameras near a high-resolution panoramic camera. While the panoramic camera provides the basic functionality of serving different viewing requests, the PTZ camera is managed by our algorithm to improve the overall video quality that may affect users watching details. The video resolution improvements from using different camera management strategies are compared in the experimental section.

FlySPEC is a video camera system designed for real-time remote operation. A hybrid design combines the high resolution of an optomechanical video camera with the wide field of view always available from a panoramic camera. The control system integrates requests from multiple users so that each controls a virtual camera. The control system seamlessly integrates manual and fully automatic control. It supports a range of options from untended automatic to full manual control. The system can also learn control strategies from user requests. Additionally, the panoramic view is always available for an intuitive interface, and objects are never out of view regardless of the zoom factor. We present the system architecture, an information-theoretic approach to combining panoramic and zoomed images to optimally satisfy user requests, and experimental results that show the FlySPEC system significantly assists users in a remote inspection tasks.

Article Share on LiteMinutes: an Internet-based system for multimedia meeting minutes Authors: Patrick Chiu FX Palo Alto Laboratory, 3400 Hillview Avenue, Bldg. 4, Palo Alto, CA FX Palo Alto Laboratory, 3400 Hillview Avenue, Bldg. 4, Palo Alto, CAView Profile , John Boreczky FX Palo Alto Laboratory, 3400 Hillview Avenue, Bldg. 4, Palo Alto, CA FX Palo Alto Laboratory, 3400 Hillview Avenue, Bldg. 4, Palo Alto, CAView Profile , Andreas Girgensohn FX Palo Alto Laboratory, 3400 Hillview Avenue, Bldg. 4, Palo Alto, CA FX Palo Alto Laboratory, 3400 Hillview Avenue, Bldg. 4, Palo Alto, CAView Profile , Don Kimber FX Palo Alto Laboratory, 3400 Hillview Avenue, Bldg. 4, Palo Alto, CA FX Palo Alto Laboratory, 3400 Hillview Avenue, Bldg. 4, Palo Alto, CAView Profile Authors Info & Claims WWW '01: Proceedings of the 10th international conference on World Wide WebMay 2001 Pages 140–149https://doi.org/10.1145/371920.371971Online:01 April 2001Publication History 40citation1,043DownloadsMetricsTotal Citations40Total Downloads1,043Last 12 Months45Last 6 weeks6 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

This paper presents a method for generating compact pictorial summarizations of video. We developed a novel approach for selecting still images from a video suitable for summarizing the video and for providing entry points into it. Images are laid out in a compact, visually pleasing display reminiscent of a comic book or Japanese manga. Users can explore the video by interacting with the presented summary. Links from each keyframe start video playback and/or present additional detail. Captions can be added to presentation frames to include commentary or descriptions such as the minutes of a recorded meeting. We conducted a study to compare variants of our summarization technique. The study participants judged the manga summary to be significantly better than the other two conditions with respect to their suitability for summaries and navigation, and their visual appeal.

Hitchcock is a system that allows users to easily create custom videos from raw video shot with a standard video camera.In contrast to other video editing systems, Hitchcock uses automatic analysis to determine the suitability of portions of the raw video.Unsuitable video typically has fast or erratic camera motion.Hitchcock first analyzes video to identify the type and amount of camera motion: fast pan, slow zoom, etc.Based on this analysis, a numerical "unsuitability" score is computed for each frame of the video.Combined with standard editing rules, this score is used to identify clips for inclusion in the final video and to select their start and end points.To create a custom video, the user drags keyframes corresponding to the desired clips into a storyboard.Users can lengthen or shorten the clip without specifying the start and end frames explicitly.Clip lengths are balanced automatically using a spring-based algorithm.

This paper describes a method for finding segments in video-recorded meetings that correspond to presentations. These segments serve as indexes into the recorded meeting. The system automatically detects intervals of video that correspond to presentation slides. We assume that only one person speaks during an interval when slides are detected. Thus these intervals can be used as training data for a speaker spotting system. An HMM is automatically constructed and trained on the audio data from each slide interval. A Viterbi alignment then resegments the audio according to speaker. Since the same speaker may talk across multiple slide intervals the acoustic data from these intervals is clustered to yield an estimate of the number of distinct speakers and their order. This allows the individual presentations in the video to be identified from the location of each presenter's speech. Results are presented for a corpus of six meeting videos.

This paper presents methods for automatically creating pictorial video summaries that resemble comic books. The relative importance of video segments is computed from their length and novelty. Image and audio analysis is used to automatically detect and emphasize meaningful events. Based on this importance measure, we choose relevant keyframes. Selected keyframes are sized by importance, and then efficiently packed into a pictorial summary. We present a quantitative measure of how well a summary captures the salient events in a video, and show how it can be used to improve our summaries. The result is a compact and visually pleasing summary that captures semantically important events, and is suitable for printing or Web access. Such a summary can be further enhanced by including text captions derived from OCR or other methods. We describe how the automatically generated summaries are used to simplify access to a large collection of videos.

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This paper describes a technique for segmenting video using hidden Markov models (HMM). Video is segmented into regions defined by shots, shot boundaries, and camera movement within shots. Features for segmentation include an image-based distance between adjacent video frames, an audio distance based on the acoustic difference in intervals just before and after the frames, and an estimate of motion between the two frames. Typical video segmentation algorithms classify shot boundaries by computing an image-based distance between adjacent frames and comparing this distance to fixed, manually determined thresholds. Motion and audio information is used separately. In contrast, our segmentation technique allows features to be combined within the HMM framework. Further, thresholds are not required since automatically trained HMMs take their place. This algorithm has been tested on a video data base, and has been shown to improve the accuracy of video segmentation over standard threshold-based systems.

Many techniques can extract information from an multimedia stream, such as speaker identity or shot boundaries. We present a browser that uses this information to navigate through stored media. Because automatically-derived information is not wholly reliable, it is transformed into a time-dependent “confidence score. ” When presented graphically, confidence scores enable users to make informed decisions about regions of interest in the media, so that non-interesting areas may be skipped. Additionally, index points may be determined automatically for easy navigation, selection, editing, and annotation and will support analysis types other than the speaker identification and shot detection used here. 1.1 Keywords Content-based retrieval, video, speaker identification, automatic analysis, visualization, skimming 2.

This paper describes research issues in the design and implementation of a video-on-demand system. Issues related to database design and population, user interface design, media formats, tertiary storage and video file server management, video delivery, and copyrights are discussed.